Automated document filtration and prioritization for document searching and access

ABSTRACT

Computer based methods, systems, and computer readable media for classifying documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to render document sections visible to machine readers. Document subsets may be generated based on user-defined terms. The machine readable documents may be classified within the content repository into one of a group of categories, based-upon the number of times classification terms appear in a specific document section of the document. Documents may be ranked based upon the frequency of classification terms in the specific section. Documents may be associated with specific diseases such as cancer, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.

TECHNICAL FIELD

Present invention embodiments relate to automated document filtration, and more specifically, to classifying documents within a corpus of documents into respective categories using automated filtration techniques to intelligently search and access content.

DISCUSSION OF THE RELATED ART

Databases and article repositories often contain a large corpus of documents of varying types of information. For example, a user may search NCBI's PubMed® database for different types of peer-reviewed scientific and clinical documents. However, while PubMed® has a robust classification system for clinical documents, the database does not classify other types of documents adequately.

Additionally, access to full-length research documents in PubMed® is often granted only if an institutional license agreement has been implemented with the journal's publisher or another form of payment has been submitted to acquire the rights to the full-length document. To ensure the accuracy of the data, users must be able to evaluate figures, graphs, tables and text within the results section of the documents. In some cases, content repositories may maintain over two million documents with no intelligent way to access the content.

Other content repositories do not provide user interfaces for specific content searching. Accordingly, large quantities of information may reside in various content repositories with limited accessibility.

SUMMARY

According to embodiments of the present invention, methods, systems and computer readable media are provided for intelligently accessing various types of information in a content repository.

Computer-based methods, systems, and computer readable media for classifying documents within a content repository or documents within a document subset are provided. Documents may be pre-processed to render document sections visible to machine readers. Document subsets may be generated based on user-defined terms. The documents within the content repository or documents within the document subsets may be classified into one or more categories (e.g., functional, clinical, case reports, review articles, or meetings and proceedings abstracts), based upon a number of times classification terms appear in a specific section of the document or from an article type tag. Documents may be ranked based upon the frequency of classification terms in the specified section. Documents may be associated with specific diseases such as cancer or cancer-types, genes, gene variants, and drugs as well as synonyms thereof by comparing relevant search/query terms to specific sections of the documents.

It is to be understood that the Summary is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilized to designate like components.

FIG. 1 is a block diagram of an example computing environment for the document filtration system, according to embodiments of the present disclosure.

FIGS. 2A-2E are flow diagrams showing classification of a document into a respective category of documents, according to embodiments of the present disclosure.

FIG. 3 is a flow diagram showing weighting of matching classification terms from a custom classification terms list for classification of the document, according to embodiments of the present disclosure.

FIG. 4 is an example genomics and therapeutics application, according to embodiments of the present disclosure.

FIG. 5 is a high level flow diagram of the document filtration system, according to embodiments of the present disclosure.

DETAILED DESCRIPTION

Methods, systems, and computer readable media are provided to classify documents in a content repository and documents within document subsets into respective categories. A user interface, which may be within a document management portal, enables the user to search for documents in a content repository based upon document categories (e.g., clinical, functional, case report, review article, or meeting and proceeding abstract), as well as gene names, gene variant names, drug names, cancer-types, and other entities including gene name synonyms, gene variant name synonyms, drug name synonyms and cancer-type name synonyms or any combination of the preceding.

An example environment for use with present invention embodiments is illustrated in FIG. 1. Specifically, the environment includes one or more server systems 10, and one or more client or end-user systems 20. Server systems 10 and client systems 20 may be remote from each other and communicate over a network 35. The network may be implemented by any number of any suitable communications media (e.g., wide area network (WAN), local area network (LAN), Internet, Intranet, etc.). Alternatively, server systems 10 and client systems 20 may be local to each other, and communicate via any appropriate local communication medium (e.g., local area network (LAN), hardwire, wireless link, Intranet, etc.).

Client systems 20 enable users to access documents (e.g., functional documents, clinical documents, case studies, review articles, or meeting and proceeding abstracts, etc.) from server systems 10 for analysis and review. The server system may include a document filtration system 15 to classify documents in order to select and prioritize relevant information.

A database system 40 may store various information for the filtration (e.g., content repository 42, classified documents 44, unclassified documents 46, custom classification terms lists 48, metadata 49, etc.). The database system may be implemented by any conventional or other database or storage unit, may be local to or remote from server systems 10 and client systems 20, and may communicate via any appropriate communication medium (e.g., local area network (LAN), wide area network (WAN), Internet, hardwire, wireless link, Intranet, etc.). The client systems may present a graphical user (e.g., GUI, etc.) or other interface (e.g., command line prompts, menu screens, etc.) to solicit information from users pertaining to the desired documents and filtration, and may provide reports including filtration results (e.g., percentage of documents classified into a respective category, percentage of documents not classified into a respective category, number of terms of a custom classification terms list found in a document, etc.).

Server systems 10 and client systems 20 may be implemented by any conventional or other computer systems preferably equipped with a display or monitor 26, a base (e.g., including at least one processor 16, 22 one or more memories 17, 23 and/or internal or external network interfaces or communications devices 18, 24 (e.g., modem, network cards, etc.)), optional input devices (e.g., a keyboard, mouse or other input device) and/or user interface 19, 25 and any commercially available and custom software (e.g., server/communications software, document filtration system 15, browser/interface software, etc.).

Alternatively, one or more client systems 20 may analyze documents to determine document classification when operating as a stand-alone unit. In a stand-alone mode of operation, the client system stores or has access to the content repository 42 and custom classification terms lists 48 as well as the document filtration system 15. The graphical user (e.g., GUI, etc.) or other user interface (e.g., command line prompts, menu screens, etc.) may solicit information from a corresponding user pertaining to the document filtration, and may provide reports including classification results and document ranking (e.g., percentage of documents classified into a respective category, percentage of documents not classified into a respective category, number of terms of a custom classification terms list found in a document, etc.).

Document filtration system 15 may include one or more modules or units to perform the various functions of present invention embodiments described below. The various modules (e.g., document pre-processing module 71, document sectioning module 72, classifier term matching module 73, weighting/classification module 74, drug and gene subset module 75, etc.) may be implemented by any combination of any quantity of software and/or hardware modules or units, and may reside within memory 17 of the server and/or client systems for execution by processor 16.

The document pre-processing module 71 may render the documents readable by a machine reader. In some aspects, optical character recognition may be used to recognize text in a document, to render the text readable and searchable. Additionally, text in tables, images, image captions, or lists may also be rendered machine readable. This processing ensures that images of documents, e.g., scanned PDFs, are included in the analysis.

The drug and gene subset module 75 filters content (documents) of the content repository 42 to generate drug subsets and gene subsets of documents. A list of drugs or genes may be obtained, for example, by a subject matter expert, and used to filter the content of the content repository. If a drug name is found in a document, that document is added to the document subset of drugs. If a gene name is found in a document, that document is added to the document subset of genes. In some aspects, the documents may be preprocessed using the preprocessing module 71 prior to filtration by module 75. The document subsets may be provided to the document sectioning module for further processing and analysis. Similar operations may be performed for other entities including gene name synonyms, gene variant name synonyms, drug name synonyms, and cancer-type name synonyms.

The document sectioning module 72 may be used to identify sections of machine readable documents. In some aspects, a document section may identified by an appropriate header. For example, the header “abstract” may indicate the presence of an abstract. The header “introduction” or “background” may indicate the presence of a section describing the current state of the art and/or background to help the reader better understand the context and rationale of the current study. The header “material and methods” may indicate an experimental section that describes the materials and methods and experimental protocols used during the course of experiments. The header “results” may indicate the presence of a results section showing data generated from the experiments. The header “discussion” may indicate the presence of a discussion section which interprets the experimental results. The header “conclusion” may indicate a summary of the experimental results of the document and future areas of investigation.

In some aspects, a section labeled with a header may be further divided into subsections having sub-headings. For example, the abstract may additional contain sub-headers such as “objective”, “methods”, “results”, and “conclusion”. In some aspects, subsections of specific subheadings may be targeted to analyze content for specific custom classification terms.

The classifier term matching module 73 may search specific sections of each document for terms in a custom classification terms list. For example, a clinical classification term list may contain single terms or phrases that may be used to identify the document as a clinical document. As another example, a functional classification term list may contain single terms or phrases that may be used to identify the document as a functional document. Similar term lists may be provided for review articles, conference proceedings and abstracts, and case studies. In some aspects, both functional and clinical information may be needed to determine the significance of a given biologic relationship. Functional information provides evidence regarding a gene's and/or gene variant's function, while clinical information provides evidence regarding a patient's response to treatment with a targeted therapy (clinical studies). For instance, the materials and methods section may be searched with custom-designed “functional query terms” to identify and classify functional articles. The abstract may be searched with custom-designed “clinical query terms” to identify and classify clinical articles.

The weighting/classification module 74 may classify different documents within the content repository and documents within document subsets into different categories. In some aspects, weighting scores may be used to classify documents as functional or clinical documents. Based on the number of terms or phrases identified in the specific section(s) of the document, a weighting score may be determined (see, FIG. 3). If the weighting score is above a classification threshold value, then the document may be classified into a respective category. Documents may be ranked for presentation to the user based upon the number of times a unique classification term appears in the methods section (for functional articles) or in the abstract section (for clinical articles). The weighting classification module 74 may also classify documents into respective categories (e.g., case study, review article, or conference proceedings and abstract) based on article type tags and/or weighting scores.

In some aspects, once classified, the documents may be stored in designated locations within database 40 (e.g., within classified documents 44), such that functional documents are located in a first directory, clinical documents are located in a second directory, and so forth. Alternatively, documents may be maintained in the same location within the content repository, but associated with metadata 49 that indicates whether the document is classified and the respective category that the document has been classified into.

Documents that the system is not able to classify may be stored in unclassified documents 46. In some aspects, these documents may be moved into a corresponding directory for unclassified documents. Alternatively, documents may be maintained in the same location within the content repository, but associated with metadata 49 that indicates that the documents are unclassified. These documents may be subject to manual review.

FIGS. 2A-2E show various flow charts for classifying documents or document subsets into respective categories. Different types of documents (e.g., scientific publications and clinical articles, review articles, case reports, or meeting/proceeding abstracts, etc.) have certain physical publishing layout requirements including providing various types of data in discrete sections of the document, typically in a predefined order. Sections of the document may also be defined by the publishing requirements, and may include the title, abstract, introduction, materials and methods, analysis/results, and discussion/conclusion sections. These documents may reside in a content repository, wherein the documents are not classified into a category.

According to present invention embodiments, the documents are rendered machine readable, so that the headings and corresponding text can be processed by the filtration system 15. Each section may contain specific types of information. Accordingly, limiting the search for custom classification terms to particular sections ensures that the documents are classified correctly.

A rules-based model may be used to search in specific sections of a document to classify the type of document (e.g., an unstructured biomedical research article). The sectioned documents can be searched, for example, for user defined custom classification terms within the text of specific sections. Based on the search results, the filtration system can classify the documents in the content repository into a respective category. The rules-based filtering system is configured to search in specific sections of a document to ensure the data originated in the current study, rather than being provided as a reference to another document.

FIG. 2A shows a flowchart for classifying clinical documents or document subsets. Clinical documents may provide evidence that a specific biologic relationship has important therapeutic ramifications. For example, a clinical document may contain a phrase such as “overexpression of erbb2 causes breast cancer” or “pertuzumab increases the rate of pathological complete response HER2-positive breast cancer”.

To identify clinical documents, a drug subset of documents is generated at operation 205. A list of drug names may be provided to the drug and gene subset module 75, from the custom classification terms lists 48. Documents of the content repository are filtered using the list of drug names, and if a term (drug name) is found, the document is added to the drug subset of documents. At operation 210, a document is obtained from the content repository. At operation 225, a particular section of the document (e.g., the abstract section) may be searched for custom clinical classification terms and/or phrases. For example, a clinical filter comprising custom classification terms or phrases may be applied to the abstract portions of the documents of the content repository to identify clinical documents. If a threshold condition is met (e.g., the weighting score is greater than a clinical threshold value), at operation 230, the document is classified as clinical. Otherwise, the document may be evaluated for classification into a different category (e.g., functional, case study, review article, conference type and proceedings abstract, etc.) at operation 235.

In some aspects, the document may be moved into a directory associated with clinical documents. Alternatively, the document may remain in the content repository and may be associated with metadata indicating that the document is a clinical document.

FIG. 2B shows a flowchart for classifying functional documents. Functional documents may provide evidence that a specific genomic alteration is oncogenic or promotes oncogenic properties. For example, a functional document may contain a phrase such as “HER2 transmembrane domain mutations (V659/G660) that stabilize homo- and heterodimerization are rare oncogenic drivers”. To identify functional documents, the materials and methods section may be searched using a functional filter for custom functional classification terms.

To identify functional documents, a gene subset of documents is generated at operation 305. A list of gene names may be provided to the drug and gene subset module 75, from the custom classification terms lists 48. Documents of the content repository are filtered using the list of gene names, and if a term (gene name) is found, the document is added to the gene subset of documents. At operation 310, a document is obtained from the content repository. At operation 325, a particular section of the document (e.g., the methods and materials section) may be searched for custom functional classification terms and/or phrases. For example, a functional filter comprising custom classification terms or phrases may be applied to the materials and methods portions of the documents of the content repository to identify functional documents. If a threshold condition is met (e.g., the weighting score is greater than a functional threshold value), at operation 330, the document is classified as functional. Otherwise, the document may be evaluated for classification into a different category (e.g., clinical, case study, review article, conference type and proceedings abstract, etc.) at operation 335. In some aspects, the document may be moved into a directory associated with functional documents. Alternatively, the document may remain in the content repository and may be associated with metadata indicating that the document is a functional document.

FIG. 2C shows a flowchart for classifying review articles. Review articles may summarize other research documents. For example, a review article may contain references to a plurality of other research documents with a related theme, such as “Kidney Cancer: Recent Advances and Future Directions”. To identify review articles, the cover page, title, or header may be searched using a review filter for custom review classification terms.

To identify review articles, a document is obtained from the content repository at operation 410. At operation 415, the system determines whether an article type tag is associated with the document that indicates that the document is a review article. In some cases, review articles, conference proceedings and abstracts, and case studies may have an article type tag identifying the type of article. In other cases, clinical and functional studies may not have an article type tag as these categories of documents may contain both types of information in different sections. If such a tag is found, at operation 420, the system classifies the document as a review article. If an article type tag is not found, a particular section of the document (e.g., the title, cover page, headings) may be searched for custom review classification terms and/or phrases at operation 425. For example, a review filter comprising custom classification terms or phrases may be applied to the cover page, title, or headers of the documents of the content repository to identify review articles. For example, review articles generally include the phrase “review article” or equivalent on their front/cover page to indicate that the document is a review article. If a review article term is present, at operation 430, the document is classified as a review article. Otherwise, the document may be evaluated for classification into a different category (e.g., functional, case study, clinical, conference type and proceedings abstract, etc.) at operation 435.

In some aspects, the document may be moved into a directory associated with review articles. Alternatively, the document may remain in the content repository and be associated with metadata indicating that the document is a review article.

FIG. 2D shows a flowchart for classifying case reports. Case reports may provide information about a single patient, or in some cases, a small number of patients. These documents may not contain a large enough sample size representative of a population, and therefore, may skew data if not properly identified. For example, a case report (or case summary) may discuss a single patient outcome, such as “Kartagener syndrome—case report”. To identify case reports, the cover page, title, or header may be searched using a case report filter for custom case report classification terms.

To identify a case report, a document is obtained from the content repository at operation 510. At operation 515, the system determines whether an article type tag is associated with the document that indicates that the document is a case report. An article type tag is typically a numeric identifier associated with documents in a database such as PubMed® or Medline® that identifies the document as a case report. If such a tag is found, at operation 520, the system classifies the document as a case report. If an article type tag is not found, a particular section of the document (e.g., the title, cover page, headings) may be searched for custom review classification terms and/or phrases at operation 525. For example, a case report filter comprising custom classification terms or phrases may be applied to the cover page, title, or headers of the documents of the content repository to identify a case report. For example, a case report generally include the phrase “case report” on their front/cover page to indicate that the document is a case report. If a case report term is present, at operation 530, the document is classified as a case report. Otherwise, the document may be evaluated for classification into a different category (e.g., functional, review article, clinical, conference type and proceedings abstract, etc.) at operation 535.

In some aspects, the document may be moved into a directory associated with case reports. Alternatively, the case report may remain in the content repository and be associated with metadata indicating that the document is a case report.

FIG. 2E shows a flowchart for classifying conference proceedings and abstracts. Conference proceedings and abstracts are short documents providing an overview of a presentation or poster from a conference. For example, conference proceedings and abstracts may contain a short summary of a research topic, such as “12^(th) Annual Biotechnology Meeting: abstract collection”. To identify conference proceedings and abstract documents, the cover page, title, or header may be searched using a conference proceedings and abstract filter for custom conference proceedings and abstract classification terms.

To identify conference proceedings and abstract documents, a document is obtained from the content repository at operation 610. At operation 615, the system determines whether an article type tag is associated with the document that indicates that the document is a conference proceedings and abstract article. If such a tag is found, at operation 620, the system classifies the document as a conference proceedings and abstract document. If an article type tag is not found, a particular section of the document (e.g., the title, cover page, headings) may be searched for custom conference proceedings and abstract classification terms and/or phrases at operation 625. For example, a conference proceedings and abstract filter comprising custom classification terms or phrases may be applied to the cover page, title, or headers of the documents of the content repository to identify conference proceedings and abstract documents. For example, conference proceedings and abstract documents generally include the phrase “conference proceeding” or abbreviation corresponding to the same on their front/cover page to indicate that the document is a conference proceedings and abstract. If a conference proceedings and abstract term is present, at operation 630, the document is classified as a conference proceedings and abstract. Otherwise, the document may be evaluated for classification into a different category (e.g., functional, review article, clinical, case report, etc.) at operation 635.

In some aspects, the document may be moved into a directory associated with conference proceedings and abstract documents. Alternatively, the document may remain in the content repository and be associated with metadata indicating that the document is a conference proceedings and abstract article.

In some aspects, a document may be classified both as a clinical document and as a functional document. In general, review articles, case reports and meeting/proceeding abstracts will not overlap in regards to classification. Classification may be performed in any evaluation order, such that the document may be evaluated with regard to the order shown in FIG. 2A-2E, or any other suitable order, and have any quantity of classifications.

Once all evaluations have been performed, then the process may terminate. Documents that are not identified, remain unclassified.

FIG. 3 shows example operations of weighting matched classification terms (e.g., terms from a custom classification terms list that are found in a section of a document) for classification of the document. Custom classification terms may be weighted equally or differentially, as shown in FIG. 3, and results may be aggregated to determine classification of the document.

At operation 710, the number of matching classification terms of a custom classification terms list for a specific section of the document is determined. If the number of unique terms is above a matching threshold value (e.g., seven or more unique terms), all terms are weighted equally, at operation 730. If the weighting score (e.g., a sum of each unique term times a weighting factor of 1) is above a classification threshold value, then the document is classified accordingly at operation 750. If the custom classification terms list is a functional terms list, then the document is classified as a functional document. If the custom classification terms list is a clinical term list, then the document is classified as a clinical document.

If the number of matching classification terms is below a matching threshold value, the matched terms are weighted differentially (e.g., four unique terms may be weighted with a factor of 0.3, five unique terms may be weighted with a factor of 0.6, and six unique terms may be weighted with a factor of 0.8), at operation 740. If the weighting score (e.g., sum of each unique term times a respective weighting factor) is above a classification threshold value, then the document is classified accordingly at operation 750. For example, if too few unique terms are identified, and the weighting score is below a classification threshold value, then the document will not be classified in the respective category.

FIG. 4 provides an example of the document filtration system identifying and classifying documents and documents within document subsets that provide evidence pertinent to genomic cancer medicines. A content repository 42 may be filtered to generate a gene subset 903 (including gene name synonyms and gene variant name synonyms) and a drug subset 907 (including drug name synonyms), which may be respectively searched for functional articles 910 and clinical articles 920. The functional articles may be searched for particular gene or gene variant names 930 (or synonyms thereof) to return documents that link genes or gene variants (or other entities including synonyms thereof) to function (e.g., HER2 signaling dysregulation and amplification linked to cancer). The clinical articles may be searched for particular drug names 940 (or synonyms thereof) to return documents referencing drugs to treat a disease (e.g., a monoclonal antibody to bind to HER2). Thus, the present techniques provide a way to access relationships between documents, in a customized specific manner.

FIG. 5 shows a flow chart of example operations. At operation 810, documents are pre-processed to render document sections visible to machine readers. At operation 815, document subsets are generated based on user-defined terms. For example, a list of genes (including gene name synonyms and gene variant name synonyms) may be used to filter a population of documents to generate a gene subset and a list of drugs (including drug name synonyms) may be used to filter a population of documents to generate a drug subset. At operation 820, documents are classified within the content repository into one or more categories (e.g., functional, clinical, case reports, review articles, or meetings and proceedings abstracts), based upon a number of times classification terms appear in a specific section of the document or an article type tag. At operation 830, documents are ranked based upon the frequency of classification terms in the specified section. At operation 840, documents are associated with specific diseases, genes, gene synonyms, gene variants, gene variant synonyms drugs, drug name synonyms, cancer-types and cancer-type name synonyms by comparing relevant search terms to specific sections of the documents. In some aspects, diseases may include a type of cancer such as breast, lung, pancreatic, ovarian, prostate, etc. In some aspects, relevant search terms may be provided, e.g., by a subject matter expert, wherein the search terms comprise genes, gene synonyms, gene variants, gene variant synonyms, drugs, drug synonyms, diseases, disease synonyms or cancer-types and cancer-type name synonyms. Documents containing these terms in specific sections of the document may be associated with these terms.

Present techniques provide a variety of advantages over existing approaches. For example, the present system classifies and provides content from licensed documents immediately available to the user. The system may be fully customized to allow the user to choose what type of information to target (e.g., functional documents, clinical documents, etc. in combination with one or more genes, gene synonyms, gene variants, gene variant synonyms, targeted drugs, drug synonyms and cancer-types and cancer-type synonyms, etc.). To obtain different content, a user may change the custom classification terms lists or generate a new custom classification terms list to obtain targeted information. In some aspects, the terminology used for classification may be curated by experts in the field and may include gene names, gene synonym names, gene variant names, gene variant synonym names, gene targeted drug names, gene targeted drug synonym names, cancer-type names and cancer-type synonym names. Further, the results may be optionally ranked accordingly to relevancy of documents. For example, functional articles may be ranked based on the frequency of functional classifier terms in the materials and methods section of the article, while clinical articles may be ranked based on the frequency of clinical functional classifier terms in the abstract section of the article. By classifying the documents or document subsets based on a frequency of custom classification terms in a document, relevant content may be delivered to a user. Patients may be matched with specific functional mutations (e.g., genomic mutations that cause cancer, resistance mutations, etc.) with optimal clinical treatment for those factors. Present techniques also offer enhanced searching and new capabilities as a user can access particular types of content. In particular, users can access strictly functional articles or strictly clinical articles.

It will be appreciated that the embodiments described above and illustrated in the drawings represent only a few of the many ways of implementing embodiments for filtering, classifying and ranking articles using a rules-based approach.

The environment of the present invention embodiments may include any number of computer or other processing systems (e.g., client or end-user systems, server systems, etc.) and databases or other repositories arranged in any desired fashion, where the present invention embodiments may be applied to any desired type of computing environment (e.g., cloud computing, client-server, network computing, mainframe, stand-alone systems, etc.). The computer or other processing systems employed by the present invention embodiments may be implemented by any number of any personal or other type of computer or processing system (e.g., desktop, laptop, PDA, mobile devices, etc.), and may include any commercially available operating system and any combination of commercially available and custom software (e.g., browser software, communications software, server software, document filtration system, etc.). These systems may include any types of monitors and input devices (e.g., keyboard, mouse, voice recognition, etc.) to enter and/or view information.

It is to be understood that the software (e.g., document filtration system 15 and document pre-processing module 71, document sectioning module 72, classifier term matching module 73, weighting/classification module 74, drug and gene subset module 75, etc.) of the present invention embodiments may be implemented in any desired computer language and could be developed by one of ordinary skill in the computer arts based on the functional descriptions contained in the specification and flow charts illustrated in the drawings. Further, any references herein of software performing various functions generally refer to computer systems or processors performing those functions under software control. The computer systems of the present invention embodiments may alternatively be implemented by any type of hardware and/or other processing circuitry.

The various functions of the computer or other processing systems may be distributed in any manner among any number of software and/or hardware modules or units, processing or computer systems and/or circuitry, where the computer or processing systems may be disposed locally or remotely of each other and communicate via any suitable communications medium (e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection, wireless, etc.). For example, the functions of the present invention embodiments may be distributed in any manner among the various end-user/client and server systems, and/or any other intermediary processing devices. The software and/or algorithms described above and illustrated in the flow charts may be modified in any manner that accomplishes the functions described herein. In addition, the functions in the flow charts or description may be performed in any order that accomplishes a desired operation.

The software of the present invention embodiments (e.g., document filtration system 15 and document pre-processing module 71, document sectioning module 72, classifier term matching module 73, weighting/classification module 74, drug and gene subset module 75, etc.) may be available on a non-transitory computer useable medium (e.g., magnetic or optical mediums, magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memory devices, etc.) of a stationary or portable program product apparatus or device for use with stand-alone systems or systems connected by a network or other communications medium.

The communication network may be implemented by any number of any type of communications network (e.g., LAN, WAN, Internet, Intranet, VPN, etc.). The computer or other processing systems of the present invention embodiments may include any conventional or other communications devices to communicate over the network via any conventional or other protocols. The computer or other processing systems may utilize any type of connection (e.g., wired, wireless, etc.) for access to the network. Local communication media may be implemented by any suitable communication media (e.g., local area network (LAN), hardwire, wireless link, Intranet, etc.).

The system may employ any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., content repository 42, classified documents 44, unclassified documents 46, custom classification terms lists 48, metadata 49, etc.). The database system may be implemented by any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., content repository 42, classified documents 44, unclassified documents 46, custom classification terms lists 48, metadata 49, etc.). The database system may be included within or coupled to the server and/or client systems. The database systems and/or storage structures may be remote from or local to the computer or other processing systems, and may store any desired data (e.g., content repository 42, classified documents 44, unclassified documents 46, custom classification terms lists 48, metadata 49, etc.).

The present invention embodiments may employ any number of any type of user interface (e.g., Graphical User Interface (GUI), command-line, prompt, etc.) for obtaining or providing information (e.g., content repository 42, classified documents 44, unclassified documents 46, custom classification terms lists 48, metadata 49, etc.), where the interface may include any information arranged in any fashion. The interface may include any number of any types of input or actuation mechanisms (e.g., buttons, icons, fields, boxes, links, etc.) disposed at any locations to enter/display information and initiate desired actions via any suitable input devices (e.g., mouse, keyboard, etc.). The interface screens may include any suitable actuators (e.g., links, tabs, etc.) to navigate between the screens in any fashion.

The report may include a listing of prioritized documents along with any other information arranged in any fashion, and may be configurable based on rules or other criteria to provide desired information to a user (e.g., article analytics, weighting scores, search terms, etc.).

The present invention embodiments are not limited to the specific tasks or algorithms described above, but may be utilized for any application in which custom filtration is needed to identify and extract specific relationships in a content repository comprising unstructured documents.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, “including”, “has”, “have”, “having”, “with” and the like, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises a document of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 

What is claimed is:
 1. A method for classifying documents within a content repository comprising: pre-processing documents to render document sections visible to machine readers; generating document subsets from the documents based on user-defined terms; classifying documents within the content repository or documents within the document subsets into one or more categories, based upon a number of times classification terms appear in a specific section of the document or an article type tag, wherein classifying documents based on the number of times classification terms appear in the specific section of the document comprises applying predefined weighting values to the number of times classification terms appear in the specific section, wherein the predefined weighting values are selected based on a specific number equal to a sum of unique classification terms in the specific section compared to a matching threshold value of a plurality of matching threshold values, wherein each matching threshold value is associated with a specific section of the document; ranking documents based upon the frequency of classification terms in the specified section; and associating documents with specific diseases, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.
 2. The method of claim 1, wherein the categories are selected from the group consisting of functional, clinical, case reports, reviews, or meetings and proceedings abstracts.
 3. The method of claim 1, wherein the article type tag specifies the category of the document.
 4. The method of claim 1, wherein classification terms are provided by a custom classification terms list, wherein for functional and clinical articles, when the sum of unique classification terms identified in a section of the document exceeds the matching threshold value, weighting the classification terms equally; and when the sum of unique classification terms identified in a section of the document is less than the matching threshold value, weighting the classification terms differentially.
 5. The method of claim 1, wherein the specific section used to classify an article as clinical is an abstract section.
 6. The method of claim 1, wherein the specific section used to classify an article as functional is a methods and materials or experimental section.
 7. The method of claim 1, wherein a document is classified as both a clinical and a functional document.
 8. A computer system for classifying documents within a content repository wherein the system comprises at least one processor configured to: pre-process documents to render document sections visible to machine readers; generating document subsets from the documents based on user-defined terms; classify documents within the content repository or documents within the document subsets into one or more categories, based upon a number of times classification terms appear in a specific section of the document or an article type tag, wherein classifying documents based on the number of times classification terms appear in the specific section of the document comprises applying predefined weighting values to the number of times classification terms appear in the specific section, wherein the predefined weighting values are selected based on a specific number equal to a sum of unique classification terms in the specific section compared to a matching threshold value of a plurality of matching threshold values, wherein each matching threshold value is associated with a specific section of the document; rank documents based upon the frequency of classification terms in the specified section; and associate documents with specific diseases, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.
 9. The computer system of claim 8, wherein the categories are selected from the group consisting of functional, clinical, case reports, reviews, or meetings and proceedings abstracts.
 10. The computer system of claim 8, wherein the article type tag specifies the category of the document.
 11. The computer system of claim 8, wherein classification terms are provided by a custom classification terms list, wherein for functional and clinical articles, when the sum of unique classification terms identified in a section of the document exceeds the matching threshold value, weighting the terms equally; and when the sum of unique classification terms identified in a section of the document is less than a matching threshold value, weighting the terms differentially.
 12. The computer system of claim 8, wherein the specific section used to classify an article as clinical is an abstract section.
 13. The computer system of claim 8, wherein the specific section used to classify an article as functional is a methods and materials or experimental section.
 14. The computer system of claim 8, wherein a document is classified as both a clinical and a functional document.
 15. A computer program product for classifying documents in a content repository, the computer program product comprising one or more computer readable storage media collectively having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to: pre-process documents to render document sections visible to machine readers; generating document subsets from the documents based on user-defined terms; classify documents within the content repository or documents within the document subsets into one or more categories, based upon a number of times classification terms appear in a specific section of the document or based on an article type tag, wherein classifying documents based on the number of times classification terms appear in the specific section of the document comprises applying predefined weighting values to the number of times classification terms appear in the specific section, wherein the predefined weighting values are selected based on a specific number equal to a sum of unique classification terms in the specific section compared to a matching threshold value of a plurality of matching threshold values, wherein each matching threshold value is associated with a specific section of the document; rank documents based upon the frequency of classification terms in the specified section; and associate documents with specific diseases, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.
 16. The computer program product of claim 15, wherein the categories are selected from the group consisting of functional, clinical, case reports, reviews, or meetings and proceedings abstracts.
 17. The computer program product of claim 15, wherein the article type tag specifies the category of the document.
 18. The computer program product of claim 15, wherein classification terms are provided by a custom classification terms list, wherein for functional and clinical articles, when the sum of unique classification terms identified in a section of the document exceeds the matching threshold value, weighting the classification terms equally; and when the sum of unique classification terms identified in a section of the document is less than the matching threshold value, weighting the classification terms differentially.
 19. The computer program product of claim 15, wherein the specific section used to classify an article as clinical is an abstract section.
 20. The computer program product of claim 15, wherein the specific section used to classify an article as functional is a methods and materials or experimental section. 